On Learning Higher-Order Consistency Potentials for Multi-Class Pixel Labeling

Abstract

Pairwise Markov random fields are an effective framework for solving many pixel labeling problems in computer vision. However, their performance is limited by their inability to capture higher-order correlations. Recently proposed higher-order models are showing superior performance to their pairwise counterparts. In this paper, we derive two variants of the higher-order lower linear envelop model and show how to perform tractable move-making inference in these models. We propose a novel use of this model for encoding consistency constraints over large sets of pixels. Importantly these pixel sets do not need to be contiguous. However, the consistency model has a large number of parameters to be tuned for good performance. We exploit the structured SVM paradigm to learn optimal parameters and show some practical techniques to overcome huge computation requirements. We evaluate our model on the problems of image denoising and semantic segmentation.